[Perceived utility of the inclusion of physician assistants in the surgical process quality and continuing education in Germany : Results of an interprofessional online survey]. / Empfundener Nutzen der Einbindung von Physician Assistants in die chirurgische Prozessqualität und Weiterbildung in Deutschland : Ergebnisse einer interprofessionellen Online-Umfrage.

BACKGROUND: Compared to Anglo-American countries, physician assistants (PA) remain an underrepresented professional group within the German healthcare system. In the surgical disciplines, PAs may relieve the administrative burden of doctors by taking on delegable routine tasks, thus creating time and resources for advanced surgical training. OBJECTIVE: According to interprofessional experts, can the use of PA lead to an optimization of surgical training and a gain in time for surgical qualification in Germany? MATERIAL AND METHODS: After searching for systematic reviews of the current state of knowledge, an online survey was initiated among surgeons and PAs via social networks to determine current and desired clinical areas of activity for PAs in surgery and their future influence on specialist training in Germany. RESULTS: A total of nine systematic reviews were identified, suggesting a beneficial impact of PAs on length of stay, direct costs, and treatment outcomes in surgical scenarios. The online survey included 234 surgeons and 114 PAs. Hospitals with ≥ 90 surgical beds employed PAs far more frequently (65%) than smaller institutions (40%). Although both professional groups are generally highly satisfied with the integration of PAs into clinical workflows, there are gradually different opinions about the preferred spectrum of tasks and duties. DISCUSSION: PAs would like to have greater responsibility in ordering and interpreting diagnostic tests, communicating with patients, and working in the operating theater. Surgeons are concerned that PAs could replace surgical interns and residents. PAs may enrich healthcare in Germany on various levels and can also improve surgical training. The voice and needs of all professional groups must be considered and respected during the upcoming health system reform.

Summary of the 2022 ACR Intersociety Meeting.

The ACR Intersociety Committee meeting of 2022 (ISC-2022) was convened around the theme of "Recovering From The Great Resignation, Moral Injury and Other Stressors: Rebuilding Radiology for a Robust Future." Representatives from 29 radiology organizations, including all radiology subspecialties, radiation oncology, and medical physics, as well as academic and private practice radiologists, met for 3 days in early August in Park City, Utah, to search for solutions to the most pressing problems facing the specialty of radiology in 2022. Of these, the mismatch between the clinical workload and the available radiologist workforce was foremost-as many other identifiable problems flowed downstream from this, including high job turnover, lack of time for teaching and research, radiologist burnout, and moral injury.

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians.

The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI's performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI's ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians' intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

Predicting physician burnout using clinical activity logs: Model performance and lessons learned.

BACKGROUND: Burnout is a significant public health concern affecting more than half of the healthcare workforce; however, passive screening tools to detect burnout are lacking. We investigated the ability of machine learning (ML) techniques to identify burnout using passively collected electronic health record (EHR)-based audit log data. METHOD: Physician trainees participated in a longitudinal study where they completed monthly burnout surveys and provided access to their EHR-based audit logs. Using the monthly burnout scores as the target outcome, we trained ML models using combinations of features derived from audit log data-aggregate measures of clinical workload, time series-based temporal measures of EHR use, and the baseline burnout score. Five ML models were constructed to predict burnout as a continuous score: penalized linear regression, support vector machine, neural network, random forest, and gradient boosting machine. RESULTS: 88 trainee physicians participated and completed 416 surveys; greater than10 million audit log actions were collected (Mean [Standard Deviation] = 25,691 [14,331] actions per month, per physician). The workload feature set predicted burnout score with a mean absolute error (MAE) of 0.602 (95% Confidence Interval (CI), 0.412-0.826), and was able to predict burnout status with an average AUROC of 0.595 (95% CI 0.355-0.808) and average accuracy 0.567 (95% CI 0.393-0.742). The temporal feature set had a similar performance, with MAE 0.596 (95% CI 0.391-0.826), and AUROC 0.581 (95% CI 0.343-0.790). The addition of the baseline burnout score to the workload features improved the model performance to a mean AUROC of 0.829 (95% CI 0.607-0.996) and mean accuracy of 0.781 (95% CI 0.587-0.936); however, this performance was not meaningfully different than using the baseline burnout score alone. CONCLUSIONS: Current findings illustrate the complexities of predicting burnout exclusively based on clinical work activities as captured in the EHR, highlighting its multi-factorial and individualized nature. Future prediction studies of burnout should account for individual factors (e.g., resilience, physiological measurements such as sleep) and associated system-level factors (e.g., leadership).

The patient encounter index: a novel method of measuring clinical workload in a paediatric cardiology service.

Technological advances have led to better patient outcomes and the expansion of clinical services in paediatric cardiology. This expansion creates an ever-growing workload for clinicians, which has led to workflow and staffing issues that need to be addressed. The objective of this study was the development of a novel tool to measure the clinical workload of a paediatric cardiology service in Cape Town, South Africa: The patient encounter index is a tool designed to quantify clinical workload. It is defined as a ratio of the measured duration of clinical work to the total time available for such work. This index was implemented as part of a prospective cross-sectional study design. Clinical workload data were collected over a 10-day period using time-and-motion sampling. Clinicians were contractually expected to spend 50% of their daily workload on patient care. The median patient encounter index for the Western Cape Paediatric Cardiac Service was 0.81 (range 0.19-1.09), reflecting that 81% of total contractual working time was spent on clinical activities. This study describes the development and implementation of a novel tool for clinical workload quantification and describes its application to a busy paediatric cardiology service in Cape Town, South Africa. This tool prospectively quantifies clinical workload which may directly influence patient outcomes. Implementation of this novel tool in the described setting clearly demonstrated the excessive workload of the clinical service and facilitated effective motivation for improved allocation of resources.

Nutrition delivery, workload and performance in a model-based ICU glycaemic control system.

BACKGROUND AND OBJECTIVE: Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. However, most ICU GC protocols only change insulin interventions, making the variable nutrition aspect of STAR less clinically desirable. This paper compares the performance of STAR modulating only insulin, with three simpler alternative nutrition protocols in clinically evaluated virtual trials. METHODS: Alternative nutrition protocols are fixed nutrition rate (100% caloric goal), CB (Cahill et al. best) stepped nutrition rate (60%, 80% and 100% caloric goal for the first 3 days of GC, and 100% thereafter) and SLQ (STAR lower quartile) stepped nutrition rate (65%, 75% and 85% caloric goal for the first 3 days of GC, and 85% thereafter). Each nutrition protocol is simulated with the STAR insulin protocol on a 221 patient virtual cohort, and GC performance, safety and total intervention workload are assessed. RESULTS: All alternative nutrition protocols considerably reduced total intervention workload (14.6-19.8%) due to reduced numbers of nutrition changes. However, only the stepped nutrition protocols achieved similar GC performance to the current variable nutrition protocol. Of the two stepped nutrition protocols, the SLQ nutrition protocol also improved GC safety, almost halving the number of severe hypoglycaemic cases (5 vs. 9, P = 0.42). CONCLUSIONS: Overall, the SLQ nutrition protocol was the best alternative to the current variable nutrition protocol, but either stepped nutrition protocol could be adapted by STAR to reduce workload and make it more clinically acceptable, while maintaining its proven performance and safety.

Nutrition delivery of a model-based ICU glycaemic control system.

BACKGROUND: Hyperglycaemia is commonplace in the adult intensive care unit (ICU), associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a proven, effective model-based ICU GC protocol that uniquely maintains normo-glycaemia by changing both insulin and nutrition interventions to maximise nutrition in the context of GC in the 4.4-8.0 mmol/L range. Hence, the level of nutrition it provides is a time-varying estimate of the patient-specific ability to take up glucose. METHODS: First, the clinical provision of nutrition by STAR in Christchurch Hospital, New Zealand (N = 221 Patients) is evaluated versus other ICUs, based on the Cahill et al. survey of 158 ICUs. Second, the inter- and intra- patient variation of nutrition delivery with STAR is analysed. Nutrition rates are in terms of percentage of caloric goal achieved. RESULTS: Mean nutrition rates clinically achieved by STAR were significantly higher than the mean and best ICU surveyed, for the first 3 days of ICU stay. There was large inter-patient variation in nutrition rates achieved per day, which reduced overtime as patient-specific metabolic state stabilised. Median intra-patient variation was 12.9%; however, the interquartile range of the mean per-patient nutrition rates achieved was 74.3-98.2%, suggesting patients do not deviate much from their mean patient-specific nutrition rate. Thus, the ability to tolerate glucose intake varies significantly between, rather than within, patients. CONCLUSIONS: Overall, STAR's protocol-driven changes in nutrition rate provide higher nutrition rates to hyperglycaemic patients than those of 158 ICUs from 20 countries. There is significant inter-patient variability between patients to tolerate and uptake glucose, where intra-patient variability over stay is much lower. Thus, a best nutrition rate is likely patient specific for patients requiring GC. More importantly, these overall outcomes show high nutrition delivery and safe, effective GC are not exclusive and that restricting nutrition for GC does not limit overall nutritional intake compared to other ICUs.